Skin Cancer Classification
14 papers with code • 1 benchmarks • 1 datasets
ISIC 2020 Challenge Dataset
Latest papers with no code
Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-based Non-invasive Digital System
Skin cancer is a global health concern, necessitating early and accurate diagnosis for improved patient outcomes.
Joint-Individual Fusion Structure with Fusion Attention Module for Multi-Modal Skin Cancer Classification
Most convolutional neural network (CNN) based methods for skin cancer classification obtain their results using only dermatological images.
Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: A Review
As lesions within the same individual typically share similarities and follow a predictable pattern, an ugly duckling naevus stands out as unusual and may indicate the presence of a cancerous melanoma.
Domain shifts in dermoscopic skin cancer datasets: Evaluation of essential limitations for clinical translation
The limited ability of Convolutional Neural Networks to generalize to images from previously unseen domains is a major limitation, in particular, for safety-critical clinical tasks such as dermoscopic skin cancer classification.
Multi-class Skin Cancer Classification Architecture Based on Deep Convolutional Neural Network
Furthermore, we develop five different stacking models such as inceptionv3-inceptionv3, Densenet-mobilenet, inceptionv3-Xception, Resnet50-Vgg16, and stack-six for classifying the skin lesions and found that the stacking models perform poorly.
CIFF-Net: Contextual Image Feature Fusion for Melanoma Diagnosis
In this paper, based on contextual image feature fusion (CIFF), a deep neural network (CIFF-Net) is proposed, which integrates patient-level contextual information into the traditional approaches for improved Melanoma diagnosis by concurrent multi-image comparative method.
Adversarial Attacks and Defences for Skin Cancer Classification
There has been a concurrent significant improvement in the medical images used to facilitate diagnosis and the performance of machine learning techniques to perform tasks such as classification, detection, and segmentation in recent years.
Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets
Instead, we evaluate Siamese Neural Networks (SNNs), which not only allows us to classify images of skin lesions, but also allow us to identify those images which are different from the trained classes - allowing us to determine that an image is not an example of our training classes.
Reversing Skin Cancer Adversarial Examples by Multiscale Diffusive and Denoising Aggregation Mechanism
Crucially, to further reverse adversarial noises and suppress redundant injected noises, a novel multiscale denoising mechanism is carefully designed that aggregates image information from neighboring scales.
Comparison of Deep Learning and Machine Learning Models and Frameworks for Skin Lesion Classification
The MobileNet model was trained separately using both TensorFlow and PyTorch frameworks.
